An Enhanced Approach for Preprocessing of Mammogram Images using Inverse Daubechies Wavelet Transform and Non-Linear Diffusion

Abstract

Breast cancer is the most leading cause of death in women nowadays. Screening mammography is currently the best available radiological technique for early detection of breast cancer. The detection of breast cancer is disturbed due to the existence of artifacts which reduce the rate of accuracy. For this reason, the pre-processing of mammogram images is very important in the process of breast cancer analysis because it reduces the number of false positives. This paper discusses about two existing filtering techniques and compares it with the results of a proposed filtering method. It is used to solve the noise removal problems and separate the background region from the breast profile region using an automatic thresholding technique. The results are evaluated on the pre-processing method on a set of images obtained from MIAS database. Thus this preparation phase improves the image quality and accentuates the CAD results more accurately.

Automatic elimination of the pectoral muscle in mammograms based on anatomical features

“…For this reason, some automatic methods have been proposed in the literature with the objective of reducing clinical analysis errors and radiologist discrepancies, although all these methods are still quite improvable with respect to the final output. Kowsalya et al [5] commented on the difficulty of interpreting digital mammograms without a preprocessing phase, so they recommend performing a processing stage before applying any analysis to the mammogram, since it is essential to find the edges of the region of interest without deviations from background. This problem is particularly evident in the mediolateral oblique view (MLO) where labels, wedges and pectoral muscle could disrupt focus on the breast tissue.…”